analytics-zoo release 0.5.0

Highlights

analytics-zoo release 0.4.0

Highlights

• Support for BigDL 0.7.2 and Spark 2.4; see the download page for all the supported versions.
• Initial OpenVINO support for the model serving API, which can use OpenVINO toolkit to accelerate the inference speed for the TensorFlow models on Analytics Zoo. please refer to the related document and example for more details.
• Initial Persistent Memory support for distributed deep learning training, which can leverage Intel Optane DC Persistent Memory to cache large training data set; please refer to the related document for more details.
• Various new features, including additional built-in models (such as sequence-to-sequence model and unsupervised time series anomaly detection model), learning rate schedule in Adam, Spark ML vector support in nnframes, TensorFlow Keras model support in TFOptimizer, etc.

analytics-zoo release 0.3.0

Highlights

• Distributed TensorFlow (both training and inference) on Spark, which supports:

  • Data wrangling and analysis using PySpark
  • Deep learning model development using TensorFlow or Keras
  • Distributed training/inference on Spark and BigDL
  • All within a single unified pipeline and in a user-transparent fashion!

• More support for text processing and models, including:

  • Common feature engineering operations for text data (such as tokenization, normalization, padding, etc.)
  • Word Embedding layers that directly load pretrained GloVe model
  • Text matching models (such as KNRM)

• Various improvements and new features, such as:

  • Support for trainable variable (Parameter)
  • Support for Keras objectives (with zero-based label)
  • Improvements to model serving APIs
  • Improvements to example and use case documents

analytics-zoo release 0.2.0

Highlights

• Support for both BigDL 0.5.0 and BigDL 0.6.0
• New reference use case (image similarity based house recommendation)
• Additional pre-trained models (Inception v3, MobileNet v2, quantized models)
• Improved support for autograd and custom loss/layer
• Improved support for model serving APIs

analytics-zoo release 0.1.0

Highlights

• Support for building and productionizing end-to-end deep learning applications for big data
• E2E analytics + deep learning pipelines (natively in Spark DataFrames and ML Pipelines) using nnframes
• Flexible model definition using autograd, Keras & transfer learning APIs
• Data preprocessing using built-in feature engineering operations
• Out-of-the-box solutions for a variety of problem types using built-in deep learning models and reference use cases
• Serving models using POJO model serving APIs for web services and other big data frameworks (e.g., Storm or Kafka)